Comparative studies on using RSM and TOPSIS methods to optimize residential air conditioning systems

doi:10.1016/j.energy.2017.11.160

Energy

Volume 144, 1 February 2018, Pages 98-109

https://doi.org/10.1016/j.energy.2017.11.160 Get rights and content

Highlights

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Energy consumption (Q_c) and thermal comfort (PMV) of a TAC system were evaluated.
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The optimization aim was to achieve thermally comfort at lowest energy consumption.
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RSM and TOPSIS methods were applied for operating optimization of the TAC system.
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RSM method used 9 cases saving 74% of computation cost compared to TOPSIS.

Abstract

The operating parameters of task/ambient air conditioning (TAC) systems including supply air temperature (t_s) and air flow rate (Q_s) were reported to have critical effects on energy savings and thermally comfortable environment. Due to the existing contradictions between these two aspects, a multi-objective study should be carried out to realize consuming minimum energy and at the same time to guarantee the thermal comfort level at suitable range. Two optimization methods were adopted in this study, one is the response surface methodology (RSM), and the other is the technique for order preferences by similarity to an ideal solution (TOPSIS) method. The objective of this study was to compare the pros and cons of these two methods. It was found that the optimum operating parameters obtained using RSM method were 26 °C (t_s) and 28.94 l/s (Q_s), corresponding to energy consumption (Q_c) of 46.89 W and PMV of 0.11; while that obtained using TOPSIS method were 26 °C (t_s) and 30 l/s (Q_s), corresponding to energy consumption (Q_c) of 49.64 W and PMV of 0.09. Furthermore, compared with TOPSIS method, there were only 9 cases used in RSM method saving 74% of computation cost.

Introduction

A task/ambient air conditioning (TAC) system was defined as a space conditioning system that permits thermal environment in a small and local area to be individually controlled [1], [2]. In recent years, the TAC system, due to its better performance in terms of energy saving and flexible control over thermal environments [3], [4], [5], [6], [7], has attracted continuously increasing attentions, and a large number of studies have been carried out. Therein, the operating parameters: supply air temperature and air flow rate of the TAC system as an important issue can significantly affect energy utilization and thermal comfort levels [8]. Hence, a number of investigations have been carried out on the influences of operating parameters and operations of TAC systems [9], [10].

(1)
Studies on thermal comfort

Parts of these studies focused on the influence of operating parameters on thermal comfort of the TAC system. Bauman et al. [1] studied a floor-based TAC system and found that local supply temperatures be kept at above 17 °C to prevent uncomfortable cold draft. Gong et al. [11], [12] suggested different supply air velocities should be adjusted corresponding to different supply air temperatures for a task∖ambient supply outlet. Niu et al. [13] investigated a chair-based personalized ventilation system with supply air temperatures at 15 to 22 °C, and suggested that the air flow rate can be limited below 1.2 l/s to prevent the draft risk. Faculdade et al. [14] evaluated a desk based personalized ventilation system in a classroom. The air temperature was set at 28 °C, and air flowrate was set at 60.3 m³/h at upper supplier and 45.6 m³/h at lower supplier. Melikov [15] investigated on a seat headrest-incorporated personalized ventilation system, and reported that this system can provide an environment with 2 °C to 3 °C cooler than the room air to improve the occupants’ thermal comfort. In addition to these TAC systems at daytime, efforts were also made on TAC systems in sleeping environments. Pan et al. [8] developed a novel bed-based TAC system installed in a bedroom in subtropical Hong Kong. It was found that raising supply air temperature or reducing supply air flow rate can increase the PMV values. Ahmed et al. [16] studied a novel local exhaust ventilation system for an office room, PMV and PPD were used to evaluate human thermal comfort conditions in the occupied zone. It was found a significant improvement with an acceptable thermal comfort was achieved. Mao et al. [7] also carried out research on a TAC system equipped in a bedroom, and stated that the variation of supply air conditions had impacts on the draft risk in the occupied zone. Furthermore, to understand the operating of a TAC system, comfort lines with combination of different values of t_s and Q_s were proposed in a following-up study. It was reported that the operating under this comfort line can help the TAC system maintain thermally neutral environment [17].

(2)
Studies on energy consumption

On the other hand, some studies focused on the influence of operating parameters on energy consumption of the TAC systems. Two types of TAC systems applied to bedrooms were developed previously [7], [8]. Both of these studies indicate that increasing supply air flow rate can obviously enhance the energy saving potential. Stefano et al. [18] studied a personalized ventilation system in an office in hot and humid climates. The results show that, adjusting the supply air temperature at 24 °C, supply air flow rate at 2.5 l/s and room set temperature at 28 °C can achieve the highest energy saving performance (51%). A TAC system with supply outlet at different heights was also studied, and it was found that the energy utilization coefficient was increased by 100% with the supply air flow rate was raised from 20 l/s to 80 l/s [19].

(3)
Optimization of TAC system operations

As mentioned above, different operating conditions were investigated in these previous studies. Therein, some of the researchers reported the best conditions in energy saving or thermal comfort. However, most of them used comparisons between the different conditions. Zhang et al. [4] evaluated the thermal feeling of occupants and energy saving of the TAC system used in three cities, respectively, but the integrated effect of thermal comfort and the energy saving was not considered. It’s difficult to judge the thermal comfort status of the TAC system when reporting its energy saving.

Stefano [14] studied 36 cases for different conditions, compared the energy need values and finally found the best energy saving case. Pan et al. [8], [20] compared performances of TAC system in energy saving and thermal comfort using 28 cases, and concluded the relations between operating parameters and performance of the TAC system. However, the best operating condition was not reported in this study.

These previous studies had the same problem: how to consider the integrated effect of energy consumption and thermal comfort. To tackle with this problem, in a previous study on TAC system [21], TOPSIS method was used to investigate the energy consumption, ventilation and thermal comfort of an air conditioning system and to get a balance among these three performance aspects [15]. 45 cases with different supply conditions were designed. The case with the best overall performance was found out at a supply air temperature of 23 °C, an air flow rate of 50 l/s, a fresh air flow rate of 13 l/s, and a supply height of 1.1 m. The study showed that this method can effectively compare the different attributes, like energy consumption and thermal comfort level, and give a scientific method to integrate the different aspects. Other than the TOPSIS method, a new methodology was proposed to consider effects of both thermal comfort and energy consumption in another pervious study [22]. In this study, RSM method was applied to construct relations between the energy consumption and operating parameters, and relations between thermal comfort and operating parameters. According to requirements on minimum energy consumption and suitable thermal comfort, these equations were solved and the optimum operating parameters were obtained.

However, the above mentioned RSM and TOPSIS methods are based on different theories and have different characteristics. RSM method is a mathematical and statistical technique which builds a polynomial equation based on the experimental data and helps to describe the behavior of a data set. Its objective is to make statistical previsions. It can be well applied to the conditions which are influenced by several variables [23]. In statistics, RSM method predicts the relations between the operating parameters of an air conditioning system and one or more response variables (performance evaluation indexes), like energy consumption [24]. Different from the RSM method, the TOPSIS method [17] was used to select the best case among all the cases. The concept of TOPSIS is to choose a case that simultaneously have the shortest distance from the positive ideal solution (PIS) and the farthest distance from the negative ideal solution (NIS). Hence, this method does not care about the relation between the operating parameters and the evaluation indexes. The TOPSIS method focused on the data of evaluation indexes while the RSM method focused on the relations between the evaluation indexes and the operating parameters. Therefore, these are two methods with different characteristics. On the other hand, both of these two methods can be successfully applied to research on building environment. The RSM method was effectively used in areas related to refrigeration, fluid dynamics and building environment [25], [26], [27], and the TOPSIS method was applied in areas related to energy or building environment [28], [29], [30].

However, there are no studies paying attention to the comparisons between these two important optimization methods, especially for use in the area of air conditioning system operation optimization. Therefore, considering that these two methods have different concepts and characteristics, it’s necessary to carry out a comparative study on these two methods to understand their pros and cons. In this paper, a numerical study based on CFD approach was carried out on a previously developed bedroom TAC system [7], [15]. Firstly, the range of operating parameters was given according to previous studies [7], [15]. Secondly, 9 simulation cases were designed, and the RSM method was applied to build the predictive models of energy consumption and thermal comfort using operating parameters. The TOPSIS method was applied to select the best case from 35 simulation cases. Thirdly, the optimum operating parameters were obtained using the two methods, respectively. Finally, the difference between using these two methods were compared and analyzed.

Section snippets

Methodology

The methodology used in this study is schematically shown in Fig. 1. The objective of the current study is to optimize the operating parameters of a TAC system to realize saving energy and maintaining thermal comfort. Two optimization methods were used in the current study, one was RSM method and the other was TOPSIS method. For RSM method, firstly, according to the ranges of supply air temperature and supply air flow rate [15], [31], [32], simulation cases were determined through a

Results and analysis

The optimization was conducted using RSM and TOPSIS methods respectively and was reported as follows.

Conclusions

In the current study, two optimization methods, RSM and TOPSIS, were respectively used to optimize the energy consumption and thermal comfort of a bedroom TAC system. The advantages and disadvantages of these two methods were compared and analyzed, including the following aspects: optimization procedures, optimization results, the theoretical fundamentals and computation cost. In this study, the RSM method adopted 9 cases to establish predictive modles and then gave detailed information of the

Acknowledgements

The study was supported by “the Fundamental Research Funds for the Central Universities” (No.: 15CX02111A), “Research Foundation for Talents of China University of Petroleum (East China)” (No.: YJ201501018), “Shandong Provincial Natural Science Foundation, China” (No.: ZR2016EEQ29), “National Natural Science Foundation of China” (No.: 51606044 and 51408233), "Natural Science Foundation of Guangdong Province (No.: 2017A030313300) and The University of Tokyo. The corresponding author is grateful

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